Staff Machine Learning Engineer

About Us

Automation Anywhere is a leader in AI-powered process automation that puts AI to work across organizations. The company’s Automation Success Platform is powered with specialized AI, generative AI and offers process discovery, RPA, end-to-end process orchestration, document processing, and analytics, with a security and governance-first approach. Automation Anywhere empowers organizations worldwide to unleash productivity gains, drive innovation, improve customer service and accelerate business growth. The company is guided by its vision to fuel the future of work by unleashing human potential through AI-powered automation. Learn more at www.automationanywhere.com

Key Responsibilities:

• Develop and optimize machine learning models leveraging  NLP, Computer Vision, and GenAI.

• Architect and implement scalable ML pipelines for training, validation, deployment, and monitoring of production models.

• Drive the development of large-scale ML infrastructure, ensuring low-latency inference and efficient resource utilization across cloud and hybrid environments.

• Implement MLOps best practices, automating model training, validation, deployment, and performance monitoring.

• Work closely with data engineers, software engineers, and product teams to ensure seamless integration of ML solutions into production systems.

• Optimize ML models for performance, scalability, and efficiency, leveraging techniques like quantization, pruning, and distributed training.

• Enhance model reliability by implementing automated monitoring, CI/CD pipelines, and versioning strategies.

• Lead efforts in data acquisition and preprocessing, including annotation and refinement of datasets to improve model accuracy.

• Stay updated with state-of-the-art ML research, identifying opportunities to integrate new techniques and technologies into production systems.

• Bachelor’s or Master’s Degree in Computer Science, Data Science, or related fields. Advanced degrees are a plus.

• 6+ years of hands-on experience in building and deploying machine learning models, with a focus on NLP, Computer Vision, or GenAI solutions.

• Proven experience deploying machine learning models into production environments, ensuring high availability, scalability, and reliability.

• Proficiency with modern ML frameworks (e.g., TensorFlow, PyTorch).

• Experience in building ML pipelines and implementing MLOps for automating and scaling machine learning workflows.

• Strong programming skills in Python, R, SQL, and experience with big data technologies (e.g., Spark, Hadoop) for data processing and analytics.

• Basic proficiency in at least one cloud-based ML services (e.g., AWS SageMaker, Azure ML, Google AI Platform) for training, deploying, and scaling machine learning models.

• Hands-on experience with containerization (Docker), orchestration (Kubernetes), and model serving platforms (e.g., Triton Inference Server, ONNX) for production-ready ML deployments.

• Familiarity with end-to-end ML pipelines, including data collection, feature engineering, model training, and model evaluation.

• Knowledge of model optimization techniques (e.g., quantization, pruning) to improve inference performance on cloud or edge devices.

• Excellent problem-solving skills, with the ability to break down complex challenges in document extraction and transform them into scalable ML solutions.

• Strong communication skills, with the ability to articulate ML problems clearly and work autonomously.

Nice to Have:

• Experience in fine-tuning large language models (LLMs) and applying GenAI techniques.

• Experience with distributed training techniques to optimize large-scale model training across multiple GPUs or cloud environments.

• Familiarity with CI/CD pipelines for ML, automated model versioning, and monitoring tools for performance and drift in production models.